Xiaoman Wang
Papers
1
Total Citations
58
H-Index
1
About
Xiaoman Wang is a leading researcher in robotic manipulation and computer vision, with a focus on developing efficient deep learning models for autonomous grasping systems. Their most-cited work, "Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images" (2019, 58 citations), introduces a novel fully convolutional neural network that processes high-resolution 400×400 RGB-D images to generate pixel-wise robotic grasps. This contribution is significant for enabling robots to perform precise, real-time grasping in cluttered environments by down-sampling images for feature extraction while maintaining grasp accuracy. Wang’s approach addresses a critical challenge in robotics—balancing computational efficiency with high-resolution perception—making it a foundational reference for researchers in robotic manipulation. Their work has been widely cited for its practical impact on industrial automation and service robotics, demonstrating how deep learning can bridge the gap between perception and action. Wang’s research continues to influence the development of more intelligent, adaptive robotic systems, solidifying their reputation as a key innovator in the field.
Research Focus
Key Achievements
Top Papers
- 1